Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world's most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations' 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good. T he challenges facing our world today have grown in complexity and increasingly require large, coordinated efforts: between countries; and across a broad spectrum of governmental and non-governmental organisations (NGOs) and the communities they serve. These coordinated efforts work towards supporting the Sustainable Development Goals (SDGs) 1 , and there continues to be an important role for technology to support the developmental organisations and efforts active in this field to deliver the highest impact. Artificial intelligence (AI) and machine learning (ML) have attracted widespread interest in recent years due to a series of high-profile successes. AI has shown success in games and

Social robots, conversational agents, voice assistants, and other embodied AI are increasingly a feature of everyday life. What connects these various types of intelligent agents is their ability to interact with people through voice. Voice is becoming an essential modality of embodiment, communication, and interaction between computer-based agents and end-users. This survey presents a meta-synthesis on agent voice in the design and experience of agents from a human-centered perspective: voice-based human--agent interaction (vHAI). Findings emphasize the social role of voice in HAI as well as circumscribe a relationship between agent voice and body, corresponding to human models of social psychology and cognition. Additionally, changes in perceptions of and reactions to agent voice over time reveals a generational shift coinciding with the commercial proliferation of mobile voice assistants. The main contributions of this work are a vHAI classification framework for voice across various agent forms, contexts, and user groups, a critical analysis grounded in key theories, and an identification of future directions for the oncoming wave of vocal machines.

Social interaction might prevent or delay dementia, but little is known about the specific effects of various social activity interventions on cognition. This study conducted a single-site randomized controlled trial (RCT) of Photo-Integrated Conversation Moderated by Robots (PICMOR), a group conversation intervention program for resilience against cognitive decline and dementia. In the RCT, PICMOR was compared to an unstructured group conversation condition. Sixty-five community-living older adults participated in this study. The intervention was provided once a week for 12 weeks. Primary outcome measures were the cognitive functions; process outcome measures included the linguistic characteristics of speech to estimate interaction quality. Baseline and post-intervention data were collected. PICMOR contains two key features: 1) photos taken by the participants are displayed and discussed sequentially; and 2) a robotic moderator manages turn-taking to make sure that participants are allocated the same amount of time. Among the primary outcome measures, one of the subcategories of cognitive functions, verbal fluency significantly improved in the intervention group. Among the process outcome measures, a part of the subcategories of linguistic characteristics of speech, the amount of speech and richness of words, proportion of providing topics, questions, and answers in total utterances were larger for the intervention group. This study demonstrated for the first time the positive effects of a robotic social activity intervention on cognitive function in healthy older adults via RCT. The group conversation generated by PICMOR may improve participants’ verbal fluency since participants have more opportunity to provide their own topics, asking and answering questions which results in exploring larger vocabularies. PICMOR is available and accessible to community-living older adults.Clinical Trial Registration:https://clinicaltrials.gov/UMIN000036667, identifier UMIN000036667.

Background and Objectives: Social interaction might prevent or delay dementia, but little is known about the specific effects of various social activity interventions on cognition. This study conducted a single-site randomized controlled trial (RCT) of Photo-Integrated Conversation moderated by a Robot (PICMOR), a group conversation intervention program for resilience against cognitive decline and dementia. Research Design and Methods: In the RCT, PICMOR was compared to an unstructured group conversation condition. Sixty-five community-living older adults participated in this study. The intervention was provided once a week for 12 weeks. Primary outcome measures were the cognitive functions; process outcome measures included the linguistic characteristics of speech to estimate interaction quality. Baseline and post-intervention data were collected. PICMOR contains two key features: (i) photos taken by the participants are displayed and discussed sequentially; and (ii) a robotic moderator manages turn-taking to make sure that participants are allocated the same amount of time. Results: Among the primary outcome measures (i.e., cognitive functions), verbal fluency significantly improved in the intervention group. Among the process outcome measures (i.e., linguistic characteristics of speech), the amount of speech and richness of words were larger for the intervention group. Discussion and Implications: This study demonstrated for the first time the positive effects of a robotic social activity intervention on cognitive function in healthy older adults via RCT. The group conversation generated by PICMOR may improve participants cognitive function controlling the amount of speech produced to make it equal. PICMOR is available and accessible to community-living older adults.

Background The present study aimed to provide a basis for future research examining the neural mechanisms that underlie the beneficial effect of an intervention program, Photo-Integrated Conversation Moderated by Robots (PICMOR), on verbal fluency in older adults as identified in our previous randomized controlled trial. In this preliminary report, we conducted an additional experiment using resting-state functional magnetic resonance imaging (rsfMRI) after the intervention period. Specifically, we investigated the resting-state functional connectivity (rsFC) characteristics of the intervention group (INT) compared to the control group (CONT). Methods rsfMRI data were acquired from 31 and 30 participants in INT and CONT, respectively, after the intervention. In the analyses, two of the most important regions in verbal fluency, the left inferior and middle frontal gyri, were selected as seed regions, and the rsFCs were compared between groups. We also conducted regression analyses for rsFCs using the difference in individual phonemic verbal fluency task (PVFT) scores between the pre- and post-intervention periods (i.e., post- minus pre-intervention) as an independent variable. Results We found higher rsFC in INT than in CONT between the left inferior frontal gyrus as a seed region and the temporal pole and middle frontal gyrus. The rsFC strength between the left inferior frontal gyrus and temporal pole positively correlated with an increased PVFT score between the pre- and post-intervention periods. In contrast, we found lower rsFC in INT than in CONT between the left middle frontal gyrus as a seed region and the posterior cingulate cortex, precuneus, and postcentral gyrus. Conclusions Our findings suggest that the beneficial intervention effect of PICMOR on verbal fluency is characterized by enhanced rsFC of the left inferior frontal gyrus with semantic and executive control-related regions and suppressed rsFC between the left middle frontal gyrus and posterior cortical midline structures. No definitive conclusions can be made because of a lack of rsfMRI data before the intervention. However, this pilot study provides the candidates for rsFCs, reflecting the beneficial effects of PICMOR on the brain network involved in verbal fluency. Trial registration The trial was retrospectively registered at the UMIN Clinical Trials Registry (UMIN000036667) (May 7th, 2019).

A small bedside agent for preventing falls has been developed. It talks to a person on a bed to prevent them from getting out of bed abruptly, until a care worker arrives. This paper describes the user-oriented design process of the agent system. The development process involving users, such as nurses and caregivers, as well as older adults is described. First, hardware design, such as the outer shape, size, and function of the agent was reviewed by nurses and caregivers, mainly from a safety viewpoint. The prototype agent incorporating improvements based on their opinions was used experimentally by older adults after several review processes. Second, the software design of the agent, such as the content of voice call, was studied through multiple experiments to improve its acceptability. Lastly, the integrated model was introduced into care facilities and hospitals to investigate the practical serviceability of the system.

IntroductionModern neurotechnology research employing state-of-the-art machine learning algorithms within the so-called “AI for social good” domain contributes to improving the well-being of individuals with a disability. Using digital health technologies, home-based self-diagnostics, or cognitive decline managing approaches with neuro-biomarker feedback may be helpful for older adults to remain independent and improve their wellbeing. We report research results on early-onset dementia neuro-biomarkers to scrutinize cognitive-behavioral intervention management and digital non-pharmacological therapies.MethodsWe present an empirical task in the EEG-based passive brain-computer interface application framework to assess working memory decline for forecasting a mild cognitive impairment. The EEG responses are analyzed in a framework of a network neuroscience technique applied to EEG time series for evaluation and to confirm the initial hypothesis of possible ML application modeling mild cognitive impairment prediction.ResultsWe report findings from a pilot study group in Poland for a cognitive decline prediction. We utilize two emotional working memory tasks by analyzing EEG responses to facial emotions reproduced in short videos. A reminiscent interior image oddball task is also employed to validate the proposed methodology further.DiscussionThe proposed three experimental tasks in the current pilot study showcase the critical utilization of artificial intelligence for early-onset dementia prognosis in older adults.

Background Age-related decline in cognitive function, such as executive function, is associated with structural changes in the neural substrates, such as volume reductions in the lateral prefrontal cortex. To prevent or delay age-related changes in cognitive function, cognitive intervention methods that employ social activity, including conversations, have been proposed in some intervention studies. Interestingly, previous studies have consistently reported that verbal fluency ability can be trained by conversation-based interventions in healthy older adults. However, little is known about the neural substrates that underlie the beneficial effect of conversation-based interventions on cognitive function. In this pilot study, we aimed to provide candidate brain regions that are responsible for the enhancement of cognitive function, by analyzing structural magnetic resonance imaging (MRI) data that were additionally obtained from participants in our previous intervention study. Methods A voxel-based morphometric analysis was applied to the structural MRI data. In the analysis, the regional brain volume was compared between the intervention group, who participated in a group conversation-based intervention program named Photo-Integrated Conversation Moderated by Robots (PICMOR), and the control group, who joined in a control program based on unstructured free conversations. Furthermore, regions whose volume was positively correlated with an increase in verbal fluency task scores throughout the intervention period were explored. Results Results showed that the volume of several regions, including the superior frontal gyrus, parahippocampal gyrus/hippocampus, posterior middle temporal gyrus, and postcentral gyrus, was greater in the intervention group than in the control group. In contrast, no regions showed greater volume in the control group than in the intervention group. The region whose volume showed a positive correlation with the increased task scores was identified in the inferior parietal lobule. Conclusions Although definitive conclusions cannot be drawn from this study due to a lack of MRI data from the pre-intervention period, it achieved the exploratory purpose by successfully identifying candidate brain regions that reflect the beneficial effect of conversation-based interventions on cognitive function, including the lateral prefrontal cortex, which plays an important role in executive functions. Trial registration The trial was retrospectively registered on 7 May 2019 (UMIN Clinical Trials Registry number: UMIN000036667).